Paper: arXiv 2509.16707

Authors: Sid Ghatak, Arman Khaledian, Navid Parvini, Nariman Khaledian

Abstract

There are inefficiencies in financial markets, with unexploited patterns in price, volume, and cross-sectional relationships. While many approaches use large-scale transformers, we take a domain-focused path: feed-forward and recurrent networks with curated features to capture subtle regularities in noisy financial data. This smaller-footprint design is computationally lean and reliable under low signal-to-noise, crucial for daily production at scale. At Increase Alpha, we built a deep-learning framework that maps over 800 U.S. equities into daily directional signals with minimal computational overhead. The purpose of this paper is twofold. First, we outline the general overview of the predictive model without disclosing its core underlying concepts. Second, we evaluate its real-time performance through transparent, industry standard metrics. Forecast accuracy is benchmarked against both naive baselines and macro indicators. The performance outcomes are summarized via cumulative returns, annualized Sharpe ratio, and maximum drawdown. The best portfolio combination using our signals provides a low-risk, continuous stream of returns with a Sharpe ratio of more than 2.5, maximum drawdown of around 3%, and a near-zero correlation with the S&P 500 market benchmark. We also compare the model’s performance through different market regimes, such as the recent volatile movements of the US equity market in the beginning of 2025. Our analysis showcases the robustness of the model and significantly stable performance during these volatile periods. Collectively, these findings show that market inefficiencies can be systematically harvested with modest computational overhead if the right variables are considered. This report will emphasize the potential of traditional deep learning frameworks for generating an AI-driven edge in the financial market.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 6.0/10 (capped from 7.5: transaction costs not modelled; see penalty flags)
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper uses established deep learning architectures (feed-forward, recurrent networks) without heavy mathematical derivations, focusing on practical implementation rather than novel theory. It demonstrates high empirical rigor with detailed backtesting, risk metrics (Sharpe >2.5, max drawdown ~3%), regime analysis, and performance evaluation across 814 equities, though it omits full disclosure of proprietary model details.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Uncover & exploit subtle patterns<br>in financial markets"] --> B["Methodology<br>Domain-focused DNN: FFNN & RNNs<br>Curated Features"]
  B --> C["Data & Inputs<br>Price, Volume, Cross-sectional<br>800 US Equities Daily"]
  C --> D["Computation<br>Minimal overhead, robust to low SNR"]
  D --> E["Backtesting & Metrics<br>Sharpe Ratio, Max Drawdown, Correlation"]
  E --> F{"Outcomes"}
  F --> F1["Sharpe > 2.5"]
  F --> F2["Max Drawdown ~3%"]
  F --> F3["Near-zero correlation with S&P 500"]
  F --> F4["Stable in volatile markets"]

Cited References (13)

  1. High resolution subject-specific mitral valve imaging and modeling: Experimental and computational methods (2016) — link
  2. Algorithmic trading and financial forecasting using advanced artificial intelligence methodologies (2022)
  3. Automated trading systems statistical and machine learning methods and hardware implementation: a survey (2019)
  4. High frequency trading strategies, market fragility and price spikes: an agent based model perspective (2019)
  5. Grid trading system robot (GTSbot): A novel mathematical algorithm for trading FX market (2019)
  6. Intelligent forecasting with machine learning trading systems in chaotic intraday Bitcoin market (2020)
  7. Lookahead bias in pretrained language models (2024)
  8. R e (Visiting) Large Lan guage Models in Finance (2024)
and 5 more
  1. Can chatgpt forecast stock price movements? return predictability and large language models — → 2304.07619
  2. A review of reinforcement learning in financial applications — → 2411.12746
  3. LSTM-Based Deep Learning Model for Financial Market Stock Price Prediction (2024)
  4. Improving the Performance of Stock Price Prediction: A Comparative Study of Random Forest, XGBoost, and Stacked Generalization Approaches (2024)
  5. Fin-gan: Forecasting and classifying financial time series via generative adversarial networks (2024)

2 of 13 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.